Altered proteomics in brain extracellular vesicles from depressed individuals who died by suicide implicates synaptic processes
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a common and debilitating disorder whose molecular neurobiology remains unclear. Extracellular vesicles (EVs) are small vesicles that are released by cells and are involved in intercellular communication. They carry bioactive molecules, such as proteins, that reflect the state of their cell of origin. In this study, we sought to investigate the proteomic cargo of brain EVs from depressed individuals as compared to EVs from matched neurotypical individuals. In addition, we investigated how the EV proteomic cargo compares to the proteomic profile of bulk tissue. METHODS: Using mass spectrometry and label-free quantification, we investigated the EV and bulk tissue protein profile from anterior cingulate cortex samples from 86 individuals. We performed differential expression analysis to compare cases and controls, followed by in silico analysis to determine potential implicated functions of dysregulated proteins. RESULTS: Extracellular vesicles display distinct proteomic profiles compared to bulk tissue. Differential expression analysis showed that 70 proteins were differentially packaged in EVs in MDD, while there was no significant difference in protein levels between groups in bulk tissue. In silico analysis points to a strong role of these differential EV proteins in synaptic functions. CONCLUSION: To our knowledge, this is the first study to profile EV proteins in depression, providing novel information to better understand the pathophysiology of MDD. This work paves the way for discovering new therapeutic targets for MDD and prompts more investigations into EVs in MDD and other psychiatric disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".